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Writing study: One for Two: A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Prototype

← ICLR 2026 oral writing corpus

Guanjun Wang, Binwu Wang, Jiaming Ma, Zhengyang Zhou, Pengkun Wang, Xu Wang, Yang Wang · Official ICLR 2026 oral record

Figure, table and section ledger

Editorial contribution type: Learning & optimization algorithms (primary) · Architecture & representation design.

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF
38 physical pages
Before references / appendix
≈ 10.43 page-equivalents
Main figures / tables
6 / 5
Attached appendix figures / tables
10 / 19
Attached appendix span
≈ 21.87 page-equivalents
Before references / appendix: ≈ 27.45%References / intervening material: ≈ 15%Attached appendix: ≈ 57.55%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.
Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level headingStarts on PDF pageEstimated spanShare of entire PDF
1 INTRODUCTION1≈ 1.06 pp.2.79%
2 RELATED WORK2≈ 0.84 pp.2.21%
3 PRELIMINARIES3≈ 0.16 pp.0.42%
4 METHODOLOGY3≈ 3.21 pp.8.45%
5 EXPERIMENTS6≈ 3.94 pp.10.37%
6 CONCLUSION10≈ 0.32 pp.0.84%
ETHICS STATEMENT11≈ 0.11 pp.0.29%
REPRODUCIBILITY STATEMENT11≈ 0.21 pp.0.55%
REFERENCES11≈ 5.71 pp.15.03%
Appendix17≈ 0.97 pp.2.55%
A USE OF LLM18≈ 0.09 pp.0.24%
B RELATED WORK18≈ 1.38 pp.3.63%
C METHOD19≈ 0.21 pp.0.55%
D THEORY PROOF19≈ 5.08 pp.13.37%
E ALGORITHM PSEUDOCODE24≈ 0.72 pp.1.89%
F DEFINITION OF GRAPH CLASS IMBALANCE AND TOPOLOGICAL25≈ 0.81 pp.2.13%
G DATASET26≈ 4.07 pp.10.71%
H EXPERIMENT SETTING30≈ 1.04 pp.2.74%
I ADDITIONAL EXPERIMENTAL RESULTS31≈ 7.03 pp.18.5%
J LIMITATION DISCUSSION AND FUTURE WORK38≈ 0.46 pp.1.21%
All detected figure and table locations

Argument and organization

  1. Introduction and preliminaries

    Distinguish class-frequency imbalance from graph-size imbalance before proposing a shared remedy.

  2. Method and experiments

    Build topology encoding, perturbation and balanced prototypes, then evaluate isolated and intertwined imbalance.

  3. Appendices A–J

    Separate related work, derivations, construction protocols, extended tests and limitations.

Problem, claim and contribution

Two clearly named imbalance axes create a testable unification claim; graph-level classification is distinguished from node- and edge-level tasks.

Sections 1–3; Appendix B and F. · Main paper

Argument and section order

Main experiments separate class, topology, intertwined imbalance, ablations and 3D conformations. This organization is more informative than one aggregate leaderboard.

Sections 5.1–5.8, pages 7–10. · Main paper

Figures, captions and visual explanation

Figure 1 expands perception and balancing beneath the pipeline, a useful overview-to-detail pattern. Dense arrows, textured backgrounds and small mathematical labels reduce legibility at normal page size.

Figure 1, page 3. · Main paper

Tables, comparisons and evidence

Backbone-with/without pairs make the claimed plug-in contribution directly inspectable. The AirGraph bubble plot places accuracy, time and memory together, whereas radial bars make exact cross-dataset comparisons harder.

Table 4 and Figures 2–4, page 9. · Main paper

Appendix and reproducibility

Dataset construction tables preserve different binary, multiclass and topology splits. AirGraph's forecasting description would benefit from an explicit chronological partition and leakage protocol.

Appendices G.1.2–G.1.5, pages 26–30. · Main paper

Clarity and writing conventions

Theory should separate a variational upper bound and local Taylor approximation from exact equivalence. Bias placement after TopK and the activation-count update deserve implementation-level clarification before reuse.

Equations 20–35 and Algorithm 1, pages 20–25. · Main paper

What to reuse—and what not to infer

Transferable lesson: Organize a unification paper around independent failure axes, then test each axis and their intersection with clearly paired controls.

Boundary: Balanced prototype use alone does not establish class fairness; the theoretical reduction and discrete routing update are not independently certified. Low-imbalance results include exceptions to broad superiority wording.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked
2026-09-19
Reading status
Full text + selected visuals inspected
Main text
Pages 1–11: Sections 1–6, ethics and reproducibility; references 11–16.
Appendix / supplement
Pages 17–38: contents and Appendices A–J, including proofs, pseudocode, splits, all supplementary tables and limitations.
Visually inspected PDF pages
3, 9